scholarly journals “In-System” Fission-Events: An Insight into Puzzles of Exoplanets and Stars?

Universe ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 118
Author(s):  
Elizabeth P. Tito ◽  
Vadim I. Pavlov
Keyword(s):  
Stellar Systems ◽  
Mechanism Of Formation ◽  
Recent Proposal ◽  
Stellar Objects ◽  
Second Stage ◽  
Exoplanetary Systems ◽  
Orbital Periods ◽  
Two Stages ◽  
Insight Into

In expansion of our recent proposal that the solar system’s evolution occurred in two stages—during the first stage, the gaseous giants formed (via disk instability), and, during the second stage (caused by an encounter with a particular stellar-object leading to “in-system” fission-driven nucleogenesis), the terrestrial planets formed (via accretion)—we emphasize here that the mechanism of formation of such stellar-objects is generally universal and therefore encounters of such objects with stellar-systems may have occurred elsewhere across galaxies. If so, their aftereffects may perhaps be observed as puzzling features in the spectra of individual stars (such as idiosyncratic chemical enrichments) and/or in the structures of exoplanetary systems (such as unusually high planet densities or short orbital periods). This paper reviews and reinterprets astronomical data within the “fission-events framework”. Classification of stellar systems as “pristine” or “impacted” is offered.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals Predictions in avalanche forecasting

2000 ◽  
Vol 31 ◽  
pp. 377-381 ◽  
Author(s):  
D. M. McClung
Keyword(s):  
Field Data ◽  
Informational Entropy ◽  
Avalanche Forecasting ◽  
The Public ◽  
Second Stage ◽  
Relevant Field ◽  
Verification Of Models ◽  
Using Data ◽  
Two Stages

AbstractVerification of avalanche forecasts depends on the spatial and temporal scale of the forecast, and the classes of informational entropy of data implicit in the forecast. First I present a classification system for avalanche forecasts based on these parameters. Verification of models in avalanche forecasting may consist of two stages. Often, the first stage is to ensure that the model matches the scales (space and time) and the classification of forecast and that redundant variables and parameters are eliminated. Once that is achieved, verification can proceed to the second stage, testing the model against relevant field data and situations. I provide an example based on the public-danger scale bulletin used for warnings in the back country in North America and Europe. Using data on deaths and accidents from Alpine Europe with Bayesian statistics, I conclude the danger scale has more classes than necessary for back-country applications. This could be a first stage prior to actual verification of this experience-based model.

Automatic classification of plants based on information content

1970 ◽  
Vol 48 (4) ◽  
pp. 793-802 ◽  
Author(s):  
Laszlo Orloci
Keyword(s):  
Information Theory ◽  
Cluster Analysis ◽  
Information Content ◽  
Random Sample ◽  
Nearest Neighbor ◽  
Theory Model ◽  
Second Stage ◽  
Large Samples ◽  
Two Stages

An information theory model is described and its application is illustrated by an actual example. Classification is accomplished in two stages. The first stage includes cluster analysis of a random sample by an agglomerative method. Cluster analysis is followed by nearest neighbor sorting in the second stage whereby the clustering results are imposed on a second random sample of the same collection. The advantage of the procedure resides in the fact that large samples can be handled, and also, the classification produced in the second stage can be used, under specific restrictive assumptions, for unbiased prediction of different population properties. While the present paper is principally concerned with the technique itself, some taxonomic conclusions are also given.

Hierarchical Classification Of Expert Models Of Exercises Designed To Eliminate Specific Mistakes Occurring In Short Ski Turn

2020 ◽  
Author(s):  
Danijela Kuna ◽  
Matej Babić ◽  
Mateja Očić
Keyword(s):  
Hierarchical Classification ◽  
Vertical Position ◽  
Chi Square ◽  
Second Stage ◽  
Expert Model ◽  
Significant Difference ◽  
Hands On ◽  
Chi Square Test ◽  
Two Stages

The aim of the present study was to examine the structure of an expert model of exercises designed to eliminate the Lack of specific ski movement mistake in short ski turn, as well as offer a hierarchical classification of the expert model. For this purpose, a two-stage research was conducted. During the first stage of the research the exercises with the purpose of Lack of specific ski movement mistake elimination were designed by 20 skiing experts aged 25 to 45. By means of email and coordinated by the paper author, the experts first designed a model of 14 methodical exercises and subsequently selected the five most relevant ones, ranking them on a scale from 1 to 5. A nonparametric chi - square test (χ2) was used. The research showed there was no significant variation across the experts’eval-uation of the five most important methodical exercises (χ2 = 21,69; p = 0,06). The expert model of the most important methodical exercises for the Lack of specific ski movement mistake correction thus includes the following: Holding a ski stick under the handle, Jump turns, Hands on hips, Unbuttoned ski boots and Ski poles in vertical position in forwards. 307 skiing professionals of various levels of expertise participated in the second stage of the research, whose aim was to classify the Lack of specific ski movement mistake elimi-nation exercises. The participants’task was to rank the exercises based on their relevance. Total amounts of rank sums (ΣR) were calculated, the Kruskal-Wallis test (H-test) was car-ried out, and the corresponding levels of significance (p) were recorded, for the purpose of comparing the significance of diversity between rank sums and the expert model. The sta-tistically significant difference was found between the rank sums (ΣR) of the most eficient exercises for the Lack of specific ski movement mistake correction (H = 198,19; p < 0,001). The results obtained in the two stages of the research provide valuable insights regarding the methods of short ski turns. The hierarchical classification of the most important method-ical corrective exercises obtained from ski teachers and professionals with different levels of education and expertise yields accurate and precise data about corrective methodical exercises in the process of studying short ski turn. Any further research regarding the same object should evaluate the designed expert model of the most important methodical exer-cises, as well as their hierarchical classification, across different groups of participants.

scholarly journals Classification of Pap smear images for Cervical cancer using Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 2801-2807
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Convolutional Neural Network ◽  
Pap Smear ◽  
Severe Dysplasia ◽  
Normal Cells ◽  
Second Stage ◽  
Two Stages ◽  
Cancerous Cells

Classification of Pap smear images for cervical cancer consists of two types namely, normal and abnormal cancerous cells. The dataset involves 7 sets of classes of cancerous images which have 3 sets of normal cancerous images and 4 sets of abnormal cancerous images. The proposed work performs two stages of classification. The first stage of the work is classifying the data as normal or abnormal cancerous cells. In the second stage of the work, the class of the cancer as normal columnar, normal intermediate, normal superficial, light dysplasia, moderate dysplasia, severe dysplasia and carcinoma_in_situ are classified. The proposed work gives good results for classifying images for 3 sets of classes and 4 sets of classes for normal cells and is able to classify and detect normal and abnormal cell accurately.

Implementation of a Hybrid Classification Method for Diabetes

2019 ◽  
pp. 201-240
Author(s):  
Dilip Kumar Choubey ◽  
Sanchita Paul ◽  
Kanchan Bala ◽  
Manish Kumar ◽  
Uday Pratap Singh
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Feature Selection Technique ◽  
Second Stage ◽  
Classification Technique ◽  
Hybrid Classification ◽  
Two Stages

This chapter presents a best classification of diabetes. The proposed approach work consists in two stages. In the first stage the Pima Indian diabetes dataset is obtained from the UCI repository of machine learning databases. In the second stage, the authors have performed the classification technique by using fuzzy decision tree on Pima Indian diabetes dataset. Then they applied PSO_SVM as a feature selection technique followed by the classification technique by using fuzzy decision tree on Pima Indian diabetes dataset. In this chapter, the optimization of SVM using PSO reduces the number of attributes, and hence, applying fuzzy decision tree improves the accuracy of detecting diabetes. The hybrid combinatorial method of feature selection and classification needs to be done so that the system applied is used for the classification of diabetes.

SEGMENTATION OF LUNG PATTERNS IN HIGH-RESOLUTION COMPUTED TOMOGRAPHY IMAGES OF THE LUNG

2008 ◽  
Vol 07 (03) ◽  
pp. 265-280 ◽  
Author(s):  
ALENA SHAMSHEYEVA ◽  
ARCOT SOWMYA ◽  
PETER WILSON
Keyword(s):  
Region Of Interest ◽  
Ground Glass Opacity ◽  
High Resolution Computed Tomography ◽  
Second Stage ◽  
Knowledge Based ◽  
Automated Method ◽  
Computed Tomography Images ◽  
High Level ◽  
Two Stages

An automated method is presented for segmentation of two-dimensional HRCT images of the lung into regions of four lung patterns: normal, emphysema, honeycombing, and ground-glass opacity (GGO). Segmentation was implemented in two stages. At the first stage, pixel-wise classification of the lung area was performed using local textural features extracted by the wavelet transform. At the second stage, classification results were refined by application of knowledge-based rules. Performance of the method was compared on two sets of HRCT images: one included HRCT images with characteristic examples of lung patterns and the other consisted of unselected HRCT images that represented a model of routine operations at a general radiology practice. On the first set of images, sensitivity of the method ranged from 0.92 to 0.99, and specificity ranged from 0.96 to 0.99. On the second set of images, sensitivity and specificity were, respectively, 0.49 and 0.95 for emphysema, 0.87 and 0.55 for normal, 0.34 and 0.99 for honeycombing, and 0.57 and 0.94 for GGO. The two-stage approach allowed for simple and effective application of high-level knowledge about appearance of lung patterns on HRCT images and did not require feature and region of interest size selection for the first stage of pixel-wise lung pattern classification.

Algorithm of adaptive correction of structural landforms on global digital elevation models

2018 ◽  
Vol 937 (7) ◽  
pp. 57-64
Author(s):  
A.Y. Zhdanov ◽  
A.V. Pankin ◽  
A.V. Rentel
Keyword(s):  
High Altitude ◽  
Digital Elevation Models ◽  
Reference Network ◽  
Height Difference ◽  
Second Stage ◽  
Digital Elevation ◽  
Adaptive Correction ◽  
Dem Accuracy ◽  
Two Stages

Due to various factors, such as the interpolation step or automatic correlators specifics, global digital elevation models (DEM) often have an effect of understating the heights, which leads to inaccurate display of structural landforms e.g. ridges. The algorithm of adaptive correction of structural landforms elevation on DEM is proposed in this article. The algorithm consists of two stages. In the first stage, an automatic classification of structural forms is performed based on height difference between neighboring DEM elements. In the second stage, the DEM elements are corrected based on the assigned classes. Adaptivity of the algorithm allows to use it for any kind of terrain and elevation ranges. The algorithm was tested on the global DEM ALOS World 3D (ALOS W3D30); the accuracy was assessed by geodetic reference network and ICESat mission data. The developed algorithm shows an improvement of DEM accuracy, especially in high-altitude areas, and it also helps to reveal areas requiring additional verification.

Altered Fine Structure of B Lymphocytes Correlated with Defective Terminal Differentiation

1973 ◽  
Vol 31 ◽  
pp. 386-387
Author(s):  
Dale E. Bockman ◽  
L. Y. Frank Wu ◽  
Alexander R. Lawton ◽  
Max D. Cooper
Keyword(s):  
Immune Deficiency ◽  
B Lymphocytes ◽  
Plasma Cells ◽  
Present Report ◽  
Specific Antigen ◽  
Terminal Differentiation ◽  
Second Stage ◽  
Two Stages ◽  
Deficiency Diseases ◽  
Cell Line Generation

B-lymphocytes normally synthesize small amounts of immunoglobulin, some of which is incorporated into the cell membrane where it serves as receptor of antigen. These cells, on contact with specific antigen, proliferate and differentiate to plasma cells which synthesize and secrete large quantities of immunoglobulin. The two stages of differentiation of this cell line (generation of B-lymphocytes and antigen-driven maturation to plasma cells) are clearly separable during ontogeny and in some immune deficiency diseases. The present report describes morphologic aberrations of B-lymphocytes in two diseases in which second stage differentiation is defective.

Classification of Infant Vocalizations by Untrained Listeners

2019 ◽  
Vol 62 (9) ◽  
pp. 3265-3275
Author(s):  
Heather L. Ramsdell-Hudock ◽  
Anne S. Warlaumont ◽  
Lindsey E. Foss ◽  
Candice Perry
Keyword(s):  
Formal Education ◽  
Additional Insight ◽  
Listener Responses ◽  
Infant Vocalization ◽  
Early Infant ◽  
Infant Vocalizations ◽  
Audio Recordings ◽  
Speech And Language Development ◽  
Insight Into

Purpose To better enable communication among researchers, clinicians, and caregivers, we aimed to assess how untrained listeners classify early infant vocalization types in comparison to terms currently used by researchers and clinicians. Method Listeners were caregivers with no prior formal education in speech and language development. A 1st group of listeners reported on clinician/researcher-classified vowel, squeal, growl, raspberry, whisper, laugh, and cry vocalizations obtained from archived video/audio recordings of 10 infants from 4 through 12 months of age. A list of commonly used terms was generated based on listener responses and the standard research terminology. A 2nd group of listeners was presented with the same vocalizations and asked to select terms from the list that they thought best described the sounds. Results Classifications of the vocalizations by listeners largely overlapped with published categorical descriptors and yielded additional insight into alternate terms commonly used. The biggest discrepancies were found for the vowel category. Conclusion Prior research has shown that caregivers are accurate in identifying canonical babbling, a major prelinguistic vocalization milestone occurring at about 6–7 months of age. This indicates that caregivers are also well attuned to even earlier emerging vocalization types. This supports the value of continuing basic and clinical research on the vocal types infants produce in the 1st months of life and on their potential diagnostic utility, and may also help improve communication between speech-language pathologists and families.

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